{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T05:36:21.699380Z",
     "start_time": "2019-11-09T05:36:20.552269Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"1001\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    var cell = handle.cell;\n",
       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
       "    var server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id != null && id in Bokeh.index) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var id = msg.content.text.trim();\n",
       "            if (id in Bokeh.index) {\n",
       "              Bokeh.index[id].model.document.clear();\n",
       "              delete Bokeh.index[id];\n",
       "            }\n",
       "          }\n",
       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when a new output is added\n",
       "   */\n",
       "  function handleAddOutput(event, handle) {\n",
       "    var output_area = handle.output_area;\n",
       "    var output = handle.output;\n",
       "\n",
       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
       "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
       "      return\n",
       "    }\n",
       "\n",
       "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
       "      // store reference to embed id on output_area\n",
       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "    }\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "      var bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      var script_attrs = bk_div.children[0].attributes;\n",
       "      for (var i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "      }\n",
       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      var toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[toinsert.length - 1]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    var el = document.getElementById(\"1001\");\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "    }\n",
       "    finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.info(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(js_urls, callback) {\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = js_urls.length;\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var s = document.createElement('script');\n",
       "      s.src = url;\n",
       "      s.async = false;\n",
       "      s.onreadystatechange = s.onload = function() {\n",
       "        root._bokeh_is_loading--;\n",
       "        if (root._bokeh_is_loading === 0) {\n",
       "          console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "          run_callbacks()\n",
       "        }\n",
       "      };\n",
       "      s.onerror = function() {\n",
       "        console.warn(\"failed to load library \" + url);\n",
       "      };\n",
       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "    }\n",
       "  };var element = document.getElementById(\"1001\");\n",
       "  if (element == null) {\n",
       "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '1001' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.0.min.js\"];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.0.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.0.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.0.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.0.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.0.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.0.min.css\");\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i].call(root, root.Bokeh);\n",
       "      }if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"1001\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n    }\n    finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.info(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(js_urls, callback) {\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = js_urls.length;\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var s = document.createElement('script');\n      s.src = url;\n      s.async = false;\n      s.onreadystatechange = s.onload = function() {\n        root._bokeh_is_loading--;\n        if (root._bokeh_is_loading === 0) {\n          console.log(\"Bokeh: all BokehJS libraries loaded\");\n          run_callbacks()\n        }\n      };\n      s.onerror = function() {\n        console.warn(\"failed to load library \" + url);\n      };\n      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.getElementsByTagName(\"head\")[0].appendChild(s);\n    }\n  };var element = document.getElementById(\"1001\");\n  if (element == null) {\n    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '1001' but no matching script tag was found. \")\n    return false;\n  }\n\n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.0.min.js\"];\n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    \n    function(Bokeh) {\n      \n    },\n    function(Bokeh) {\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.0.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.0.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.0.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.0.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.0.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.0.min.css\");\n    }\n  ];\n\n  function run_inline_js() {\n    \n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(js_urls, function() {\n      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import torchvision.datasets as dsets\n",
    "import torchvision.transforms as transforms\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "from bokeh.io import show, output_notebook\n",
    "from bokeh.plotting import figure, gridplot\n",
    "from bokeh.models import LinearAxis, Range1d\n",
    "output_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GPU\n",
    "指定使用的 GPU 编号。  \n",
    "`watch -n 1 nvidia-smi` 实时查看 GPU 的运行状态。 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T05:36:21.855332Z",
     "start_time": "2019-11-09T05:36:21.701438Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.cuda.set_device(\"cuda:3\")\n",
    "torch.cuda.current_device()\n",
    "# device = torch.device(\"cuda:5\")\n",
    "# xxx.to(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-02T15:34:18.036850Z",
     "start_time": "2019-11-02T15:34:18.032937Z"
    }
   },
   "source": [
    "### Data\n",
    "通过`torchvision.datasets`下载`MNIST`数据。  \n",
    "训练集：`train=True`  \n",
    "测试集：`train=False`  \n",
    "常用的还有`torchvision.datasets.ImageFolder()`，按文件夹取图片。  \n",
    "\n",
    "`torchvision.transforms`可以对图片做处理。  \n",
    "`trans` 对两种处理进行了组合，包括化为`Tensor`、归一化。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T05:36:21.961734Z",
     "start_time": "2019-11-09T05:36:21.859049Z"
    }
   },
   "outputs": [],
   "source": [
    "trans = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T05:36:22.105163Z",
     "start_time": "2019-11-09T05:36:21.965680Z"
    }
   },
   "outputs": [],
   "source": [
    "train_dataset = dsets.MNIST(root='../dataset', train=True, transform=transforms.ToTensor(), download=True)\n",
    "test_dataset = dsets.MNIST(root='../dataset', train=False, transform=transforms.ToTensor(), download=True)\n",
    "\n",
    "batch_size = 100\n",
    "train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)\n",
    "test_loader = DataLoader(dataset=test_dataset, batch_size=1, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model\n",
    "分别定义生成器 G 和判别器 D，两者进行博弈。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T05:36:22.128111Z",
     "start_time": "2019-11-09T05:36:22.107638Z"
    }
   },
   "outputs": [],
   "source": [
    "class GAN(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.G = nn.Sequential(\n",
    "            nn.Linear(64, 256),\n",
    "            nn.LeakyReLU(0.2),\n",
    "            nn.Linear(256, 256),\n",
    "            nn.LeakyReLU(0.2),\n",
    "            nn.Linear(256, 784),\n",
    "            nn.Tanh()\n",
    "        )\n",
    "        self.D = nn.Sequential(\n",
    "            nn.Linear(784, 256),\n",
    "            nn.LeakyReLU(0.2),\n",
    "            nn.Linear(256, 256),\n",
    "            nn.LeakyReLU(0.2),\n",
    "            nn.Linear(256, 1),\n",
    "            nn.Sigmoid()\n",
    "        )\n",
    "    def forward(self, z):\n",
    "        return self.G(z)\n",
    "    def score(self, z):\n",
    "        fake_imgs = self.G(z)\n",
    "        fake_score = self.D(fake_imgs)\n",
    "        return fake_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T05:36:27.844577Z",
     "start_time": "2019-11-09T05:36:22.130294Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GAN(\n",
       "  (G): Sequential(\n",
       "    (0): Linear(in_features=64, out_features=256, bias=True)\n",
       "    (1): LeakyReLU(negative_slope=0.2)\n",
       "    (2): Linear(in_features=256, out_features=256, bias=True)\n",
       "    (3): LeakyReLU(negative_slope=0.2)\n",
       "    (4): Linear(in_features=256, out_features=784, bias=True)\n",
       "    (5): Tanh()\n",
       "  )\n",
       "  (D): Sequential(\n",
       "    (0): Linear(in_features=784, out_features=256, bias=True)\n",
       "    (1): LeakyReLU(negative_slope=0.2)\n",
       "    (2): Linear(in_features=256, out_features=256, bias=True)\n",
       "    (3): LeakyReLU(negative_slope=0.2)\n",
       "    (4): Linear(in_features=256, out_features=1, bias=True)\n",
       "    (5): Sigmoid()\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lrate = 0.0003\n",
    "epochs = 200\n",
    "\n",
    "model = GAN().cuda()\n",
    "criterion = nn.BCELoss()\n",
    "optim_G = torch.optim.Adam(model.G.parameters(), lr = lrate)\n",
    "optim_D = torch.optim.Adam(model.D.parameters(), lr = lrate)\n",
    "model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-07T03:04:23.259457Z",
     "start_time": "2019-11-07T03:04:23.255214Z"
    }
   },
   "source": [
    "在训练过程中，每经过 300 个 batch 就检验 选中的 5 个图像的生成情况。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T05:36:27.858903Z",
     "start_time": "2019-11-09T05:36:27.848103Z"
    }
   },
   "outputs": [],
   "source": [
    "def list_img(i, img, title):\n",
    "    img = img.reshape(28, 28)\n",
    "    plt.subplot(2, 5, i+1)\n",
    "    plt.imshow(img)\n",
    "    plt.title('%s' % (title))\n",
    "    \n",
    "def generate_test(inputs, title=''):\n",
    "    plt.figure(figsize=(15, 6))\n",
    "    imgs = model(inputs)\n",
    "    imgs = (imgs + 1) / 2\n",
    "    imgs.clamp(0, 1)\n",
    "    for i in range(len(inputs)):\n",
    "        list_img(i, imgs[i].cpu().detach().numpy(), title)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "注意：每次反向传播的时候都需要将参数的梯度归零。  \n",
    "`optim.step()`则在每个`Variable`的`grad`都被计算出来后，更新每个`Variable`的数值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在每次训练中都用`train_loader`中的一个`batch`作为训练数据。  \n",
    "`Tensor.cuda()` 每个 `batch` 在实际使用之前，都先移入 `GPU` 后进行计算。  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T06:20:04.463471Z",
     "start_time": "2019-11-09T05:36:27.862441Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 5 Axes>"
      ]
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       "\n",
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       "(function(root) {\n",
       "  function embed_document(root) {\n",
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Application\",\"version\":\"1.0.0\"}};\n",
       "  var render_items = [{\"docid\":\"045c0a24-35d9-476e-a453-f0c6d43a901b\",\"roots\":{\"1002\":\"173e7087-c33f-49cb-a754-34fa2641e802\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1002"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "result_d = []\n",
    "result_g = []\n",
    "test_inputs = torch.randn(5, 64).cuda()\n",
    "\n",
    "for e in range(epochs):\n",
    "    for i, (inputs, _) in enumerate(train_loader):\n",
    "        inputs = inputs.view(-1, 28*28).cuda()\n",
    "        real_labels = torch.ones(batch_size, 1).cuda()\n",
    "        fake_labels = torch.zeros(batch_size, 1).cuda()\n",
    "        \n",
    "        # Train D   loss(D(real_img), real) + loss(D(fake_img), fake)\n",
    "        real_score = model.D(inputs)\n",
    "        loss_d_real = criterion(real_score, real_labels)\n",
    "        \n",
    "        fake_score = model.score(torch.randn(batch_size, 64).cuda())\n",
    "        loss_d_fake = criterion(fake_score, fake_labels)\n",
    "        \n",
    "        optim_D.zero_grad() \n",
    "        loss_d = loss_d_real + loss_d_fake\n",
    "        loss_d.backward()\n",
    "        optim_D.step()\n",
    "        \n",
    "        # Train G  loss(D(G(z)), real)\n",
    "        fake_score = model.score(torch.randn(batch_size, 64).cuda())\n",
    "        loss_g = criterion(fake_score, real_labels)\n",
    "        \n",
    "        optim_G.zero_grad()\n",
    "        loss_g.backward()\n",
    "        optim_G.step()\n",
    "        \n",
    "        # Check generation\n",
    "        if i % 100 == 0:\n",
    "            result_d.append(float(loss_d))\n",
    "            result_g.append(float(loss_g))\n",
    "    if e % 60 == 0:\n",
    "        generate_test(test_inputs, str(e))\n",
    "fig = figure()\n",
    "fig.line(range(len(result_d)), result_d, legend='D loss', line_width=1.5)\n",
    "fig.line(range(len(result_g)), result_g, legend='G loss', line_color=\"green\")\n",
    "show(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Result\n",
    "由上图可见图像的生成趋向于清晰。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "分别导入真实图片和生成图片，查看判别器的效果。  \n",
    "判别器可以识别出大部分的真实图片是真实的，也能识别出所有生成图片是生成的。  \n",
    "因此判别器效果很好，而生成器效果很差。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T06:20:04.508140Z",
     "start_time": "2019-11-09T06:20:04.467012Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0.3235], device='cuda:3', grad_fn=<SigmoidBackward>)\n",
      "tensor([1.], device='cuda:3', grad_fn=<SigmoidBackward>)\n",
      "tensor([0.0041], device='cuda:3', grad_fn=<SigmoidBackward>)\n",
      "tensor([1.], device='cuda:3', grad_fn=<SigmoidBackward>)\n",
      "tensor([1.], device='cuda:3', grad_fn=<SigmoidBackward>)\n",
      "tensor([1.], device='cuda:3', grad_fn=<SigmoidBackward>)\n",
      "tensor([1.], device='cuda:3', grad_fn=<SigmoidBackward>)\n",
      "tensor([1.], device='cuda:3', grad_fn=<SigmoidBackward>)\n",
      "tensor([1.], device='cuda:3', grad_fn=<SigmoidBackward>)\n",
      "tensor([0.9569], device='cuda:3', grad_fn=<SigmoidBackward>)\n"
     ]
    }
   ],
   "source": [
    "for i, (img, _) in enumerate(test_loader):\n",
    "    if i > 9: break\n",
    "    img = img.view(-1, 28*28).cuda()\n",
    "    real_score = model.D(img.squeeze())\n",
    "    print(real_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T06:20:04.624035Z",
     "start_time": "2019-11-09T06:20:04.510757Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[1.3863e-01],\n",
      "        [1.8132e-04],\n",
      "        [1.5209e-03],\n",
      "        [3.6482e-02],\n",
      "        [7.8784e-04],\n",
      "        [6.9412e-05],\n",
      "        [6.1832e-03],\n",
      "        [6.0682e-03],\n",
      "        [1.6882e-02],\n",
      "        [4.2760e-02]], device='cuda:3', grad_fn=<SigmoidBackward>)\n"
     ]
    }
   ],
   "source": [
    "fake_score = model.score(torch.randn(10, 64).cuda())\n",
    "print(fake_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-03T07:12:44.915877Z",
     "start_time": "2019-11-03T07:12:44.913744Z"
    }
   },
   "source": [
    "### Save Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-11-09T06:20:04.769550Z",
     "start_time": "2019-11-09T06:20:04.626473Z"
    }
   },
   "outputs": [],
   "source": [
    "torch.save(model.state_dict(), 'gan-cuda.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Related Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [WGAN](https://zhuanlan.zhihu.com/p/25071913)\n",
    "- InfoGAN"
   ]
  }
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